Dendritic and axonal morphologies play fundamental roles in physiological brain function and pathological dysfunction by affecting synaptic integration, spike train transmission, and circuit connectivity. Incorporating existing and forthcoming experimental data into accurate, full-scale, and biologically plausible neural network simulations is important for quantitatively bridging the sub-cellular and systems-levels. We successfully designed, implemented, and freely distributed to the community computer software and databases to reconstruct, analyze, visualize, simulate, and share the 3D tree-like shape of neurons from many labeling and visualization techniques, developmental stages, and experimental conditions. We imaged by light microscopy, digitally traced, and shared new data, and we provided our peers with the electronic means of freely doing the same. Moreover, we combined those data with computational models of membrane biophysics to investigate the neuronal structure-activity relationship. We propose to expand this research approach with two specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to triple the number of shared reconstructions, adding new species, brain regions, and neuron types. Moreover, we will enhance the search functionality with a semantic engine using state-of-the-art ontologies. We will also extend the domain and format of distributed data to include circuitry, multi-channel information, and temporal sequences. The second aim is to develop a new knowledge base of neuron types in the hippocampus and entorhinal cortex by quantifying their morphological, physiological, and molecular properties from published reports. The hippocampal formation is one of the most studied brain regions, underlies autobiographic memory storage and spatial representation, and is prominently involved in devastating neurological disease, including epilepsy and Alzheimer's. Yet, our conceptual understanding of how the hippocampus works is limited compared to the wealth of available knowledge about its neurons, because it is difficult to find and integrate all relevant data scattered in thousands of papers. We will identify all published information and annotate it with specific pointers to the source documents in the peer- reviewed literature. The resulting open-source portal (Hippocampome.Org) will enable the derivation of potential circuit connectivity and the predictive simulation of network-wide spiking activity. We will make this application especially relevant to neuropathology by linking specific neuron types to diseases involving the hippocampus, and demonstrate its potential with a new model of learning disabilities based on impaired structural plasticity.